IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2404.00028.html
   My bibliography  Save this paper

Antinetwork among China A-shares

Author

Listed:
  • Peng Liu

Abstract

The correlation-based financial networks, constructed with the correlation relationships among the time series of fluctuations of daily logarithmic prices of stocks, are intensively studied. However, these studies ignore the importance of negative correlations. This paper is the first time to consider the negative and positive correlations separately, and accordingly to construct weighted temporal antinetwork and network among stocks listed in the Shanghai and Shenzhen stock exchanges. For (anti)networks during the first 24 years of the 21st century, the node's degree and strength, the assortativity coefficient, the average local clustering coefficient, and the average shortest path length are analyzed systematically. This paper unveils some essential differences in these topological measurements between antinetwork and network. The findings of the differences between antinetwork and network have an important role in understanding the dynamics of a financial complex system. The observation of antinetwork is of great importance in optimizing investment portfolios and risk management. More importantly, this paper proposes a new direction for studying complex systems, namely the correlation-based antinetwork.

Suggested Citation

  • Peng Liu, 2024. "Antinetwork among China A-shares," Papers 2404.00028, arXiv.org.
    • Handle: RePEc:arx:papers:2404.00028
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2404.00028
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    3. Liu, Peng & Zheng, Yanyan, 2022. "Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    5. Qiao Jiang & Qianting Ma & Xiaoxing Liu, 2021. "Research on the Stock Correlation Network Entropy and Its Influencing Factors in the Chinese Interbank Market," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, July.
    6. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 353-362, March.
    7. Zicheng Pan & Qianting Ma & Junfei Ding & Lei Wang, 2021. "Research on the stock correlation networks and network entropies in the Chinese green financial market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(2), pages 1-11, February.
    8. Liu, Wei & Ma, Qianting & Liu, Xiaoxing, 2022. "Research on the dynamic evolution and its influencing factors of stock correlation network in the Chinese new energy market," Finance Research Letters, Elsevier, vol. 45(C).
    9. Irena Vodenska & Alexander P. Becker & Di Zhou & Dror Y. Kenett & H. Eugene Stanley & Shlomo Havlin, 2016. "Community Analysis of Global Financial Markets," Risks, MDPI, vol. 4(2), pages 1-15, May.
    10. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    11. Seung Wook Oh & Julie A. Harris & Lydia Ng & Brent Winslow & Nicholas Cain & Stefan Mihalas & Quanxin Wang & Chris Lau & Leonard Kuan & Alex M. Henry & Marty T. Mortrud & Benjamin Ouellette & Thuc Ngh, 2014. "A mesoscale connectome of the mouse brain," Nature, Nature, vol. 508(7495), pages 207-214, April.
    12. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    13. Diego Garlaschelli & Guido Caldarelli & Luciano Pietronero, 2003. "Universal scaling relations in food webs," Nature, Nature, vol. 423(6936), pages 165-168, May.
    14. Xia, Lisi & You, Daming & Jiang, Xin & Guo, Quantong, 2018. "Comparison between global financial crisis and local stock disaster on top of Chinese stock network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 222-230.
    15. Conte, Marc N. & Kelly, David L., 2018. "An imperfect storm: Fat-tailed tropical cyclone damages, insurance, and climate policy," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 677-706.
    16. Peng Liu & Yanyan Zheng, 2022. "Precision measurement of the return distribution property of the Chinese stock market index," Papers 2209.08521, arXiv.org, revised Nov 2023.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paulus, Michal & Kristoufek, Ladislav, 2015. "Worldwide clustering of the corruption perception," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 351-358.
    2. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    3. Teh, Boon Kin & Goo, Yik Wen & Lian, Tong Wei & Ong, Wei Guang & Choi, Wen Ting & Damodaran, Mridula & Cheong, Siew Ann, 2015. "The Chinese Correction of February 2007: How financial hierarchies change in a market crash," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 225-241.
    4. Irena Vodenska & Alexander P. Becker & Di Zhou & Dror Y. Kenett & H. Eugene Stanley & Shlomo Havlin, 2016. "Community Analysis of Global Financial Markets," Risks, MDPI, vol. 4(2), pages 1-15, May.
    5. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    6. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    7. Li, Jianxuan & Shi, Yingying & Cao, Guangxi, 2018. "Topology structure based on detrended cross-correlation coefficient of exchange rate network of the belt and road countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1140-1151.
    8. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria-Artemis, 2016. "Bank supervision using the Threshold-Minimum Dominating Set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 23-35.
    9. Weibo Li & Wei Liu & Lei Wu & Xue Guo, 2021. "Risk spillover networks in financial system based on information theory," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    10. M. Raddant & T. Di Matteo, 2023. "A look at financial dependencies by means of econophysics and financial economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(4), pages 701-734, October.
    11. Cheong, Siew Ann & Fornia, Robert Paulo & Lee, Gladys Hui Ting & Kok, Jun Liang & Yim, Woei Shyr & Xu, Danny Yuan & Zhang, Yiting, 2011. "The Japanese economy in crises: A time series segmentation study," Economics Discussion Papers 2011-24, Kiel Institute for the World Economy (IfW Kiel).
    12. Fabio Vanni & Asmerilda Hitaj & Elisa Mastrogiacomo, 2024. "Enhancing Portfolio Allocation: A Random Matrix Theory Perspective," Mathematics, MDPI, vol. 12(9), pages 1-16, May.
    13. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    14. Zhu, Mingxue & Zhang, Hua & Xing, Wanli & Zhou, Xuanru & Wang, Lu & Sun, Haoyu, 2023. "Research on price transmission in Chinese mining stock market: Based on industry," Resources Policy, Elsevier, vol. 83(C).
    15. Pang, Raymond Ka-Kay & Granados, Oscar M. & Chhajer, Harsh & Legara, Erika Fille T., 2021. "An analysis of network filtering methods to sovereign bond yields during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    16. Leonidas Sandoval Junior, 2011. "A Map of the Brazilian Stock Market," Papers 1107.4146, arXiv.org, revised Mar 2013.
    17. Douglas Castilho & Tharsis T. P. Souza & Soong Moon Kang & Jo~ao Gama & Andr'e C. P. L. F. de Carvalho, 2021. "Forecasting Financial Market Structure from Network Features using Machine Learning," Papers 2110.11751, arXiv.org.
    18. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Constructing financial network based on PMFG and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 104-113.
    19. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    20. Chun-Xiao Nie, 2021. "Studying the correlation structure based on market geometry," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(2), pages 411-441, April.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2404.00028. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.